Data Visualization I & II

POL 051 - Summer Session 1 2026

Haley Daarstad

Why visualize data?

• Data carries weight in our society

• Visualizing data is an effective way to convey information, convince, argue

• Visualization can be used to tell The Truth™️ (or not)

Dataviz to inform

Dataviz to mislead

Inform? or mislead?

Making graphs in R

The Gapminder dataset

country	continent	year	lifeExp	pop	gdpPercap
Afghanistan	Asia	1952	29	8425333	779
Afghanistan	Asia	1957	30	9240934	821
Afghanistan	Asia	1962	32	10267083	853
Afghanistan	Asia	1967	34	11537966	836
Afghanistan	Asia	1972	36	13079460	740

Rows are observations

country	continent	year	lifeExp	pop	gdpPercap
Afghanistan	Asia	1952	29	8425333	779
Afghanistan	Asia	1957	30	9240934	821
Afghanistan	Asia	1962	32	10267083	853
Afghanistan	Asia	1967	34	11537966	836
Afghanistan	Asia	1972	36	13079460	740

In a dataset, rows are observations

The data we observe for Afghanistan in the year 1952

Rows are observations

id	age	degree	race	sex
1	47	Bachelor	White	Male
2	61	High School	White	Male
3	72	Bachelor	White	Male
4	43	High School	White	Female
5	55	Graduate	White	Female

In survey data, an observation is typically a person who took the survey (a respondent)

Columns are variables

country	continent	year	lifeExp	pop	gdpPercap
Afghanistan	Asia	1952	29	8425333	779
Afghanistan	Asia	1957	30	9240934	821
Afghanistan	Asia	1962	32	10267083	853
Afghanistan	Asia	1967	34	11537966	836
Afghanistan	Asia	1972	36	13079460	740

In a dataset, columns are variables

Life expectancy and GDP per capita are some of the variables in our data

The final graph

There are four variables on this graph. What are they?

The grammar of graphics

Graphs have an internal logic, or grammar that connects data to visuals

Data = variables in a dataset

Aesthetic = visual property of a graph (position, shape, color, etc.)

Geometry = representation of an aesthetic (point, line, text, etc.)

Mapping data to aesthetics

Data	Aesthetic	Geometry
GDP per capita	Position(x-axis)	Point
Life expectancy	Position (y-axis)	Point
Continent	Color	Point
Population	Size	Point

1. Take the data,

2. map it onto an aesthetic,

3. and visualize it with a geometry

In R

Data	aes()	geom_
gdpPercap	x	geom_point()
lifeExp	y	geom_point()
continent	color	geom_point()
pop	size	geom_point()

Use the variable names exactly as they appear in the data, map them onto the exact function names in R

ggplot(): our first function 😢

ggplot()

don’t fret; You will not memorize these as they appear on screen

ggplot: specify the data

ggplot(data = gap_07) 

Use aes() to map variables to aesthetics

ggplot(data = gap_07, aes(x = gdpPercap, y = lifeExp)) 

add geometries and layers using +

ggplot(data = gap_07, aes(x = gdpPercap, y = lifeExp)) + geom_point() 

mapping population to size in aes()

ggplot(data = gap_07, aes(x = gdpPercap, y = lifeExp, size = pop)) + 
  geom_point()

mapping continent to color in aes()

ggplot(data = gap_07, aes(x = gdpPercap, y = lifeExp, size = pop, color = continent)) + 
  geom_point()

Other layers: add the missing titles with labs()

ggplot(data = gap_07, aes(x = gdpPercap, y = lifeExp, size = pop, color = continent)) + 
  geom_point() + labs(x = "GDP per capita", y = "Life expectancy", 
       title = "Global wealth and health in 2007", size = "Population",
       color = "")

Notice that text is placed within quotation marks!

Other layers: add a theme

ggplot(data = gap_07, aes(x = gdpPercap, y = lifeExp, size = pop, color = continent)) + 
  geom_point() + labs(x = "GDP per capita", y = "Life expectancy", 
       title = "Global wealth and health in 2007") + 
  theme_bw()

There are many more themes, here are a few

The final formula

ggplot(data = gap_07, aes(x = gdpPercap, y = lifeExp, 
                          size = pop, color = continent)) +
  geom_point() + labs(x = "GDP per capita", y = "Life expectancy", 
       title = "Global wealth and health in 2007") +
  theme_bw()

1. Tell ggplot() the data we want to plot

2. Map all variables onto aesthetics within aes()

3. Add layers like geom_point() and theme_bw() using +

What’s that country way out on the bottom right?

🚨 Your turn: try labeling the points 🚨

1. Add labels to each point by mapping country names onto the label aesthetic within aes()

2. Add geom_text layer to your plot to plot the names

   install.packages("gapminder")
   library(gapminder)
   
   gap_07 <- gapminder %>%
     filter(year == 2007)

The basic plot

ggplot(data = gap_07, aes(x = gdpPercap, y = lifeExp, size = pop, 
                          color = continent)) + 
  geom_point()

Map country names to label aesthetic

ggplot(data = gap_07, aes(x = gdpPercap, y = lifeExp, size = pop, 
                          color = continent, label = country)) + 
  geom_point()

Plot the labels

ggplot(data = gap_07, aes(x = gdpPercap, y = lifeExp, size = pop, 
                          color = continent, label = country)) + 
  geom_point() + 
  geom_text() 

What did we do?

Data	Aesthetic	Geometry
gdpPercap	x	geom_point()
lifeExp	y	geom_point()
continent	color	geom_point()
pop	size	geom_point()
country	label	geom_text()

Take your data, map it onto an aesthetic, represent with a geometry

🇺🇸 The presidents 🇺🇸

elections_historic %>% 
  select(year, winner, win_party, ec_pct, popular_pct, two_term) %>% 
  slice_head(n = 4) %>% 
  knitr::kable(caption = "Sample of presidential elections", digits = 2)

🚨 Your turn 🚨

1. Make a plot of presidential election results using the elections_historic dataset

2. % of popular vote (x-axis, popular_pct) and % of electoral college vote (y-axis, ec_pct)

3. map the winner’s party to the color aesthetic, whether or not president served two terms to shape, and add labels to each point (use winner_label)

install.packages("socviz")
library(socviz)

US Presidents

What’s going on here?

What did we do?

Data	Aesthetic	Geometry
popular_pct	x	geom_point()
ec_pct	y	geom_point()
win_party	color	geom_point()
two_term	shape	geom_point()
winner_label	label	geom_text()

Take your data, map it onto an aesthetic, represent with a geometry

A graph for every season

• There are many graphs out there

• Each one works best in a specific context

• Each one combines different aesthetics and geometries

Key plotting questions

• What am I trying to show?

  • (a distribution, a relationship, a comparison, an amount)

• What kind of variables do I have?

  • (continuous, discrete, something in between)

• What aesthetics and geometries do I need for this plot?

  • (x-axis, y-axis, color, size, shape, etc.)

What kind of variable do I have?

General Social Survey

id	age	degree	race	num_kids
1	47	Bachelor	White	3
2	61	High School	White	0
3	72	Bachelor	White	2
4	43	High School	White	4
5	55	Graduate	White	2

• Continuous variables take on lots of values (GDP, population, income, age, etc.)

What kind of variable do I have?

General Social Survey

id	age	degree	race	num_kids
1	47	Bachelor	White	3
2	61	High School	White	0
3	72	Bachelor	White	2
4	43	High School	White	4
5	55	Graduate	White	2

• Discrete or categorical variables takes on a few values, often “qualitative” (yes/no, 1/0, race, etc.)

Graph 1 - The Scatterplot

The scatterplot visualizes the relationship between two continuous variables

Shows every point in the data, reveals trends and outliers

The grammar of scatterplots

The data

gdpPercap	lifeExp
974.58	43.83
5937.03	76.42
6223.37	72.30
4797.23	42.73
12779.38	75.32

Mapping the data

Data	Aesthetic	Geometry
gdpPercap	x	geom_point()
lifeExp	y	geom_point()

ggplot(gap_07, aes(x = gdpPercap, y = lifeExp)) +
  geom_point()

Typically, we put the cause on the x-axis and the effect on the y-axis

Scatterplots are for continuous variables

Plot is uninformative because continent is discrete (i.e., a category)

Graph 2: the time series

The time series uses a line to show you how a variable (y-axis) moves over time (x-axis)

The grammar of time series

The data

year	avg_yrs
1952	49.06
1957	51.51
1962	53.61
1967	55.68
1972	57.65

Mapping the data

Data	Aesthetic	Geometry
year	x	geom_line()
avg_yrs	y	geom_line()

Notice the new geometry, geom_line()

The time series

ggplot(life_yr, aes(x = year, y = avg_yrs)) +
  geom_line()

🚨 Your turn: 💸Recession💸 🚨

• Look at the economics dataset from the tidyverse package

• Type and run economics to see the data

• Type and run ?economics to read about the data

• Make a time series of unemployment over time

• Can you identify the recessions?

💸Recession💸

Multiple time series

Sometimes we observe multiple units over time; how can we visualize these?

country	continent	year	lifeExp	pop	gdpPercap
Bolivia	Americas	1987	57.251	6156369	2753.6915
United States	Americas	1977	73.380	220239000	24072.6321
Ghana	Africa	1992	57.501	16278738	925.0602
China	Asia	1997	70.426	1230075000	2289.2341
United States	Americas	1997	76.810	272911760	35767.4330

Start from scratch

ggplot(data = gapminder_sample) 

Add aesthetics

ggplot(data = gapminder_sample, aes(x = year, y = lifeExp)) 

Add geometry: 🤢

ggplot(data = gapminder_sample, aes(x = year, y = lifeExp)) +
  geom_line()

using color to separate lines

ggplot(data = gapminder_sample, aes(x = year, y = lifeExp, color = country)) + 
  geom_line()

Multiple time series

ggplot(data = gapminder_sample, aes(x = year, y = lifeExp, color = country)) + 
  geom_line()

These are useful for comparing trends across units (countries, places, people, etc)

Graph 3: the histogram

A histogram shows you how a continuous variable is distributed

Interpreting histograms

What do we see here?

The grammar of histograms

The data

lifeExp
43.83
76.42
72.30
42.73
75.32

Mapping the data

Data	Aesthetic	Geometry
lifeExp	x	geom_histogram()

Notice the new geometry, geom_histogram(); and that a histogram only uses the x-axis!

The histogram

ggplot(gap_07, aes(x = lifeExp)) + geom_histogram()

🚨 Your turn: organs 🫁🧠 🚨

In some countries, when you die it is assumed you want to donate your organs

• To not donate, you have to opt out

• In other countries, when you die it is assumed you do not want to donate your organs

• To not donate, you have to opt in

Sample from organdata

country	donors	opt
Finland	17.1	NA
Denmark	12.9	In
Austria	25.9	Out
Austria	23.9	Out
Italy	10.1	In

🚨 Your turn: organs 🫁🧠 🚨

Using the organdata dataset:

1. Make a histogram of country’s organ donation rate (donors)

2. Then set the fill aesthetic to opt, whether donors have to opt in or opt out of donating. How does the graph change?

organs 🫁🧠

Graph 4: the barplot

Barplots place a category (place, country, person, etc) on one axis and a quantity (amount, average, median, etc.) on another

Useful for making comparisons, highlighting differences

Why would there be differences across TV consumption?

The grammar of barplots

The data

marital	tv
No answer	2.56
Never married	3.11
Separated	3.55
Divorced	3.09
Widowed	3.91

Mapping the data

Data	Aesthetic	Geometry
tv	x	geom_col()
marital	y	geom_col()

Note

You could switch the x and y mapping around, but I think categories look better on the y-axis

The barplot

ggplot(tv, aes(y = marital, x = tv)) + 
  geom_col()

Graph 5: the boxplot

Boxplots compare distributions of continuous variables across groups

they show us more than the barplot which only gives us a quantity for each group

Compare distributions: the boxplot

Boxplots contain a lot of info 🥵:

• bold line is the median observation
• box is the middle 50% of observations
• thin lines show you min and max value, except…
• the dots, which are outlier observations

interpret

The grammar of boxplots

The data

continent	lifeExp
Asia	43.83
Europe	76.42
Africa	72.30
Africa	42.73
Americas	75.32

Mapping the data

Data	Aesthetic	Geometry
contient	y	geom_boxplot()
lifeExp	x	geom_boxplot()

Note

You could switch the x and y mapping around, but I think categories look better on the y-axis

The boxplot

ggplot(gapminder, aes(y = continent, x = lifeExp)) + geom_boxplot()

The five(-ish) graphs

Graph	aes()	geom_	Purpose
Scatterplot	x = cause, y = effect	point()	Relationships
Time series	x = date, y = variable	line()	Trends
Histogram	x = cont. variable	histogram()	Distributions
Barplot	y = category, x = quantity	col()	Compare amounts
Boxplot	y = category, x = cont. variable	boxplot()	Compare distributions

Know how and when to use which!

Making better graphs

• We’ve barely scratched the surface; there’s many more aesthetics, geometries, and layers in ggplot()

• Here are some of my favorite ones

• And some ideas for making graphs better

Showing “movement” using panels

We can use panels to show movement of a variable across time, space, etc.

What is this graph showing us?

Using facet_wrap

ggplot(gapminder, aes(x = lifeExp)) + geom_histogram()

Using facet_wrap

ggplot(gapminder, aes(x = lifeExp)) + geom_histogram() + 
  facet_wrap(vars(year)) 

Note

Make sure the facetting variable is wrapped in vars()!

Make aesthetics static

ggplot(gap_07, aes(x = gdpPercap, y = lifeExp)) + 
  geom_point(size = 4, color = "orange", shape = 2) 

Take your aesthetics out of aes() and into geom() to make them static

Ridge plots (better than grouped histograms)

library(ggridges)
ggplot(gapminder, aes(y = continent, x = lifeExp)) + geom_density_ridges() 

Ease visual comparison + kinda looks like the Joy Division album

Beeswarm plots (alternative boxplots)

library(ggbeeswarm)
ggplot(gapminder, aes(y = continent, x = lifeExp)) +  geom_quasirandom() 

Beeswarm plots tell us something boxplots don’t: the number observations by group; used recently by the NYT

Use different color and fill scales

ggplot(gapminder, aes(x = lifeExp, fill = continent)) + geom_histogram() + 
  scale_fill_brewer(palette = "Blues") 

scale_fill_brewer() for fill, scale_color_brewer for color

My favorite scale (right now)

ggplot(gapminder, aes(x = lifeExp, fill = continent)) + geom_histogram() + 
  scale_fill_viridis_d(option = "magma") 

scale_fill_viridis_d for discrete variables, scale_fill_viridis_d for continuous

Many other themes

theme_spongeBob() from tvthemes package, many more online